System and method for image acquisition using supervised high quality imaging

ABSTRACT

An image capture system and method for imaging biological samples disposed in culture media supported by a plate. The system has a calibration module, an image acquisition module and an image presentation module. When the system receives a culture plate for imaging, default values for the culture plate and media are used to begin image acquisition at a given time. The captured image is then used to create a pixel by pixel map of the image. The system inspects the pixel-by-pixel map for saturated pixels and for signal to noise ratio and acquires a new image if the number of saturated pixels is at or above a predetermined threshold or the signal to noise ratio for the pixel is below a predetermined threshold. From this inspection a new value of photon flux and/or exposure time is determined and a new image is captured using the new value and the steps are repeated. Upon a determination that a predetermined threshold signal to noise ratio has been obtained for the non-saturated pixels, or when the predetermined upper threshold for the time interval for image acquisition is elapsed the system provides a final image for the given time.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a national phase entry under 35 U.S.C. § 371of International Application No. PCT/EP2015/052017 filed Jan. 30, 2015,published in English, which claims priority from U.S. ProvisionalApplication No. 61/933,426, filed Jan. 30, 2014, all disclosure of whichare incorporated herein by reference.

BACKGROUND OF THE INVENTION

High Dynamic Range (HDR) imaging is a digital imaging technique thatcaptures a greater dynamic range between the lightest and darkest areasof an image. A process for automatically optimizing a dynamic range ofpixel intensity obtained from a digital image is described in U.S. Pat.No. 7,978,258 to Christiansen et al. HDR takes several images atdifferent exposure levels and uses an algorithm to stitch them togetherto create an image that has both dark and light spots, withoutcompromising the quality of either one. However, HDR can present adistortion of reality because it distorts the intensity of the imageoverall. Accordingly, HDR techniques that enhance contrast withoutdistorting the intensity of the image continue to be sought.

Techniques for enhancing an image of a biological sample are describedin WO 2012/152769 to Allano et al. Among the problems with imaging suchsamples identified in Allano et al. are:

i) the size of the colonies being viewed;

ii) the proximity of one colony to another;

iii) the color mix of the colonies;

iv) the nature of the Petri Dish; and

v) the nature of the culture medium; as well as other factors.

Allano et al.'s proposed solution to the problem of imaging a biologicalsample is to prepare a source image created from images obtained at eachcolor, removing predetermined absorption effects for the culture mediumand the culture vessel and determining a value for photon flux andexposure time using a predetermined exposure to obtain an image which isthen dissected into luminosity zones. From that, image luminosity isobtained and used to determine if the value for photon flux and exposuretime used was correct or if a new value for photon flux and exposuretime should be used for image capture.

Problems with the above techniques is that they do not provide a systemwith an ability to provide imaging conditions that can detect verysubtle changes in contrast that are required for image-baseddetection/identification of microbes on growth media. Becauseimage-based evidence of microbes and/or their growth on media is (or atleast can be) difficult to detect, more robust techniques for imagingsuch samples are sought.

BRIEF SUMMARY OF THE INVENTION

Described herein is a system and method that enhances the image capturefor images with low or variable contrast. One example of such achallenging imaging environment is that of bacterial colonies growing onagar growth plates. The bacterial colonies reflect the light differentlyfrom the agar. In addition, the bacterial colonies can vary from lightcolors to dark colors and reflect light differently than the agar. Thetime to capture an image of a colony is short (approximately onesecond). Typically, an image of the growth plate is taken every 3 to 6hours.

An image is acquired in a series of N image acquisitions at each timeinterval “x” (i.e. t₀, t₁ . . . t_(x)). The first acquisition (N=1) usesdefault values for the light intensity and exposure time, referred toherein as “photon flux and exposure time.” The photon flux value definesthe number of photons reaching the scene per unit time and unit area((photon quantity)·(time⁻¹)·(area⁻¹)). The time being the integrationtime at the camera's sensor. The exposure time determines the number ofphotons captured by the sensor for one frame acquisition. Said anotherway, photon flux is rate of flow of photons from the light source andexposure time influences the quantity of those photons received by thesensor for image acquisition. For a given photon flux, exposure timecontrols image intensity.

One skilled in the art is aware of many different ways to control photonflux to influence image intensity. As noted above, one techniquecontrols the exposure time of the image. There are other techniques thatcan be used to control of the intensity of the light transmitted to thesensor. For example, filters, apertures, etc. are used to control thephoton flux, which in turn, controls the intensity. Such techniques arewell known to the skilled person and not described in detail herein. Forpurposes of the embodiments of the invention described herein, the lightintensity is set constant and exposure time is the variable used tocontrol photon flux integration.

In the embodiments where photon flux is controlled by controlling theexposure time, initial exposure time values are obtained from systemcalibration. The system is calibrated using a library of calibrationplates. Baseline calibration is obtained as a function of plate type andmedia type. When the system is used to interrogate new growth plates thecalibration data for a particular plate type and media type is selected.In this regard, growth plates can be: mono-plates (i.e. for one media);bi-plates (two media); tri-plates (three media), etc. Each type ofgrowth plate present unique imaging challenges. The calibration providesa default exposure time for capturing the first image (image N=1) of thegrowth plate. The calibration also makes it possible for the system (orsystem operator) to determine which parts of the image are plate (i.e.not background) and, of the plate portions of the image, which portionsare media (the nutrients used to cultivate the colonies) and whichportions are, at least potentially, colonies.

Image N=1 of a growth plate is captured using the default valuesobtained from calibration. If an averaging technique is used to capturethe digital images of the growth plate, the bright pixels will have abetter signal-to-noise ratio (SNR) than the dark pixels. In the methoddescribed herein, signals are isolated for individual pixels, regardlessof whether the pixels are light or dark. For a predetermined number ofpixels, the intensity, exposure time and SNR are determined. A “map” ofthese values in the image context is prepared. From this map, a newexposure time that will preferably not saturate more than apredetermined fraction of pixels is selected for the N+1 imageacquisition. Preferably, an exposure time in which only a very smallfraction of pixels (or less) are saturated is determined and used tocapture the final image.

From this a map of the SNR for each pixel where the SNR is updated (i.e.the grey value is refined and the SNR improved for the non-saturatedpixels) for each non-saturated pixel is generated. An image is simulatedbased on this map.

An optimization function algorithm is used to map each grey valueintensity for each pixel to the required exposure time corresponding tothe optimal SNR for the pixel. The optimization algorithm begins bylooking at the initial image (N=1), which was captured using thepredetermined default exposure time. An intensity, exposure, and SNR mapis generated for the entire image. The exposure time for each pixel isadjusted based on image N and another image (N+1) is captured. As statedabove, the new exposure time is chosen that will saturate the signals ofthe dark parts, resulting in overexposure of the light parts. Theintensity map, exposure map, and SNR map are updated for each pixel.This is an iterative process and images are acquired until the maximumSNR for each pixel for the image is reached, or the maximum number ofimages is reached, or the maximum allotted time has been reached.

Essentially, the dark spots remain dark, the bright spots remain brightand the SNR is improved. The agar growth medium acts as the backgroundfor the digital images. A pixel in the image that is different in someway (i.e. a different intensity) from previous images indicates thateither the colony is growing or there is contamination (e.g. dust) onthe plate. This technique can be used to look at multiple plates at onetime.

As the SNR is significantly improved, details can be revealed (withconfidence) that could not be seen/trusted allowing for detection ofvery early small colonies in timed plate imaging. The systems andmethods also provide images corresponding to an optimal exposure timethat corresponds to specific and controlled saturation over the scene orobject of interest.

Once the image acquisition at time t₀ is complete, the process ofiterative image acquisition is stopped for that time interval. When thepredetermined time interval from t₀ to t₁ has elapsed, the iterativeimage acquisition process is repeated until the desired confidence inthe integrity of the image so acquired has been obtained. The signal tonoise ratio is inversely proportional to the standard deviation (i.e.SNR=gv′/standard deviation.) Therefore, an image acquisition that yieldsa maximum SNR per pixel (i.e. a minimum standard deviation per pixel)will provide an image with a high confidence associated with a time“T_(x).”. For example, a high SNR image is obtained for a plate that hasbeen incubated for four hours (T₁=4 hours). Another high SNR image ofthe same plate is obtained after the plate has been incubated for anadditional four hours (T_(x)=8 hours).

Once an image associated with a subsequent time (T_(x+1)) is obtained,that image (or at least selected pixels of the image associated with anobject of interest) can be compared with the image associated with theprevious time (T_(x)) to determine if the subsequent image providesevidence of microbial growth and to determine the further processing ofthe plate.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic description of a three module system for imageacquisition and presentation according to one embodiment of the presentinvention;

FIG. 2 is a flowchart of system operation for the three module systemillustrated in FIG. 1;

FIG. 3 is a description of the functions of the calibration moduleillustrated in FIG. 1 for illumination calibration, optics calibration,and camera calibration according to one embodiment of the presentinvention;

FIG. 4 is an illustration of the data determined from the calibrationplates to calibrate the system of FIG. 1 according to one embodiment;

FIG. 5 is a description of the functions of the image acquisition moduleillustrated in FIG. 1 according to one embodiment of the presentinvention;

FIG. 6 is a schematic of the method of image acquisition using thesystem of FIG. 1 according to one embodiment;

FIG. 7 is more detailed description of the functions performed by theimage acquisition module illustrated in FIG. 5.

FIG. 8 illustrates the method for choosing the next image acquisitiontime according to one embodiment;

FIG. 9 is a description of the steps taken to finalize imageacquisition; and

FIG. 10 is a process flow schematic of how to determine systemintegrity.

DETAILED DESCRIPTION

The system described herein is capable of being implemented in opticalsystems for imaging microbiology samples for the identification ofmicrobes and the detection of microbial growth of such microbes. Thereare many such commercially available systems, which are not described indetail herein. One example is the BD Kiestra™ ReadA Compact intelligentincubation and imaging system (2^(nd) generation BD Kiestra™ incubator).Such optical imaging platforms have been commercially available for manyyears (originally CamerA PrimerA from Kiestra® Lab Automation), and aretherefore well known to one skilled in the art and not described indetail herein. In one embodiment, the system is a non-transitorycomputer-readable medium (e.g. a software program) that cooperates withan image acquisition device (e.g. a camera), that provides high qualityimaging of an image by interacting to provide a maximum Signal to NoiseRatio (SNR) for every pixel in the image. For each pixel and each color(e.g. channel), the intensity and exposure time are recorded and thesystem then predicts the next best exposure time to improve on the SNRof the whole scene or objects of interest in the scene. One skilled inthe art will appreciate that the multiple values obtained per pixel willdepend upon the pixels and the imaging system. For example, in an RBGimaging system, values are obtained for each channel (i.e., red, green,or blue). In other systems, the values are obtained for differentspectral bands or wavelengths.

Initially, the system is calibrated. Calibration of imaging systems suchas the one described herein are well known to one skilled in the art. Avariety of calibration approaches are known. Described herein areexamples of system calibration that provide a baseline against which thecaptured images are evaluated. During calibration, calibration plates(e.g. plates with media but no colonies) are used and the system imageacquisition is calibrated against the known input. A library ofcalibration values for each type of plate media is created, and thecalibration data used for a particular plate is selected based on themedia in the test plate. Both the system and the data are calibrated.For data calibration, SNR, Linearity, Black level, etc. are determinedfor each pixel of the captured image of the calibration plate. Systemcalibration includes, but is not limited to, lens distortion, chromaticaberrations, spatial resolution, etc.

Following calibration, images of new plates are acquired. The pixels inthe image are analyzed in real time in order to estimate the exposuretime that will improve the SNR of the pixels with an SNR that is eitherbelow a predetermined threshold or for those pixels with the lowest SNR.Typical imaging systems only retain intensity values for the pixels inthe image. In the embodiments described herein, intensity and exposuretime are recorded for each pixel. The same pixel is imaged at differentexposure times and intensity information is combined to generate highSNR data. From this information, an image can be generated for anyspecified exposure time, or the best exposure time can be extracted tocontrol pixel saturation.

From a quantitative aspect, due to high SNR, the confidence on subtleintensity variations, on colors and texture is greatly improved allowinga better performance of subsequent object recognition or databasecomparison. The analysis is done on a grey scale with comparison both tothe grey value of the pixel in a prior image (i.e. for image N, thevalue of the pixel in image N−1). In addition to comparison of the samepixel grey value in the prior image, the pixel grey value of adjacentpixels is also compared with the pixel grey value to determinedifferences (e.g. the colony/media interface).

SNR of dark of colored objects is uneven in the different channels orvery poor when compared to bright objects. In order to improve on this,the system and method described herein deploy an image detection modulein which object detection is based upon contrast, SNR, andsize/resolution. SNR is improved in both dark and bright regions.Standard deviation is decreased and therefore local contrast is made assignificant in bright and dark regions. The goal here is to provide asystem that will detect even subtle differences between the x and x+1time interval images of a plate suspected to contain a growing culture.Those differences must be distinguishable from the “noise” that resultsfrom signal variations but not changes in the sample attributable to agrowing culture. The systems and methods described herein are especiallyvaluable when objects of interest in the scene may exhibit verydifferent colors and intensities (reflectance or absorbance).

Specifically, the system and method provide automatic adaptation of thedynamic range (extended dynamic range) to accommodate the scene. Thesystem and method provides both the minimum exposure time for saturatingthe brightest pixel and the maximum exposure time for saturating thedarkest pixel (within physical and electronic constraints of the imageacquisition equipment (e.g. the camera)). The system and method providefor faster convergence towards a minimum SNR per pixel when compared toimage averaging. The system and method provide for improved confidenceon colors. Specifically, the SNR for red, green and blue values arehomogenized regardless of intensity disparities in red, green, and bluecolors.

Intensity confidence intervals are known per pixel, which is veryvaluable for any subsequent classification effort. The SNR optimizationprovided by the system and method can be supervised (weighting ofdetected objects of interest to compute next image acquisition'sexposure times).

Intensity, exposure time and estimate SNR are determined fromcalibration and physics theory for each pixel. To further improve onimage quality, chromatic aberration and lens distortion are alsocalibrated and corrected to render an image free of such defects.

The system and method can control pixel SNR for the image either in anautomatic mode or a supervised mode where certain portions of the imageare of particular interest. In the automatic mode, the whole image ofthe scene is optimized, and all pixels are treated equally. In thesupervised mode, the scene is further analyzed when acquired to detectthe objects of interest. SNR maximization favors the objects of interestregions.

In automatic mode, the image acquisition will stop after the first ofthe three following conditions occurs: (1) a minimum level of SNR isreached for each and every pixel; (2) a predetermined number ofacquisitions have been performed on this scene; or (3) the maximumallowed acquisition time has been reached.

Referring to FIG. 1, a schematic of the system of one embodiment isillustrated. The system 100 has three modules. The first is a systemcalibration module 110. The calibration module calibrates theillumination of the image, the optics used to collect the image, and thebaseline data for the new plate under evaluation by the system.

The image acquisition module 120 is in communication with the systemcalibration module 110. The image acquisition module captures an imageof the object under analysis. The image is captured using exposure timeand other criteria determined in a manner described in detailhereinbelow in the context of specific examples. As discussed above,image acquisition proceeds in an iterative manner until a predeterminedSNR threshold is met for each pixel or until a predetermined number ofimages have been captured. The image presentation module 130 providesthe image with the best dynamic range (i.e. the brightest non-saturatingpixels that are just below saturation), either globally (i.e. inautomatic mode) or restricted to the objects of interest (i.e. insupervised mode).

Referring to FIG. 2, both external data and calibration plates (i.e. therange of combinations of test plates and culture media) are used tocalibrate the system). From the calibration, both system calibration anddata calibration are determined. The system and data calibration valuesare used in image acquisition for a new plate. The calibration is usedto validate the new image in terms of the image map (i.e. which pixelsare regions outside the plate, which are inside the plate but media withno colonies and which regions reveal colonies).

FIG. 3 further illustrates the specific aspects of the system equipmentthat are calibrated. For the illumination component(s) 111 the warm uptime, intensity (λ)=f (input power) and field homogeneity aredetermined. Again, for the test plates, the media should be homogeneousfor the applicable region (i.e. the entire plate for a mono-plate, halfthe plate for a bi-plate and a third of a plate for a tri-plate). Forthe optics calibration 112, alignment, chromatic aberrations andgeometrical distortions are determined. For camera calibration 113,baseline levels are determined. Such baseline data are: warm up time;linearity (fixed relationship of grey values and number of photons thatreach the sensor) and black level as functions of exposure time, SNR asa function of pixel intensity; field homogeneity; chromatic aberrations;and geometrical distortions are all determined as a baseline againstwhich the acquired image is evaluated. Such baseline data is well knownto one skilled in the art and not described in further detail.

FIG. 4 is further detail on the inputs into the calibration system (i.e.system information, the library of calibration plates and other inputs).For each calibration plate, an image is obtained and each pixel isassigned values for black level, SNR, linearity and illumination. Forthe system (i.e. not pixel by pixel) model values that reflect systemfactors such as distortion, chromatic aberrations, spatial resolutionand white balance are determined. These values are all collected toprovide a calibrated system and calibrated data for use in theevaluation of plates. As noted below, these values are used to finalizeimage acquisition.

More details about the image acquisition module are described in FIG. 5.In the first step, an image is acquired using default values. From thisfirst image, the intensity, exposure time, and SNR for each pixel aredetermined. The intensity is determined by subtracting the “black level”for the pixel from a measured intensity value. The black level and SNRare obtained from the calibration previously described.

Image acquisition occurs at times t₀, t₁, . . . t_(x). At each time, animage is acquired through a series of N image acquisitions. The seriesof N image acquisitions iterates to a SNR for the acquired image thatcorrelates with high confidence in image integrity.

Image acquisition at a given time (e.g. to) and update is illustrated inFIG. 6. The image of a new plate 610 is acquired in step 620. Imageacquisition is informed by the system 630 and data 640 calibration.Plate traffic conditions (i.e. number of plates per unit time) are alsoused to calibrate and control the system. At a later point in timeduring the image acquisition process, a subsequent image is acquired 650and compared with the prior image (either automatically or supervised).Typically, there will be about four to about ten image acquisitions ateach time interval to obtain an image with an acceptable confidence.Once the desired SNR for the selected object is obtained, the exposuretime is determined for the final image acquisition 660.

According to one embodiment, the pixels are updated as follows. Greyvalue, reference exposure time and signal to noise ratio represent theinformation stored for each illumination configuration (top, side,bottom, or a mixture of them) per plate (image object). This informationis updated after each new acquisition. To start with, this informationis updated using the first image acquisition (N=1).

Grey value, reference exposure time and signal to noise ratio representthe information stored for each illumination configuration (top, side,bottom, or a mixture of them) per plate. This information is updatedafter each new acquisition. To start with this information isinitialized according to the first image acquisition (N=1). In oneembodiment, gv_(x,y,1) is a grey value (gv) at image position (x,y)corresponding to the 1^(st) image capture (N=1) of the plate usingexposure time E₁ and respective Signal to Noise Ratio (SNR_(gv)). Inthis embodiment:

-   -   black_(x,y,E) ₁ is the black reference value point in (x,y)        corresponding to exposure time E₁;    -   E′_(x,y,1) is the updated reference time point in (x,y) after 1        acquisition;    -   gv′_(x,y,1,E) ₁ is the updated grey value in x,y after 1        acquisition at E′_(x,y,1) equivalent exposure time;    -   SNR′_(x,y,1) is the updated SNR in x,y after 1 acquisition;

E_(x, y, 1)^(′) = E₁gv_(x, y, 1, E_(x, y, 1)^(′))^(′) = gv_(x, y, 1) − black_(x, y, E₁)${SNR}_{x,y,N}^{\prime} = \left\{ {\begin{matrix}{SNR}_{{gv}_{x,y,1}} \\{0\mspace{14mu} {if}\mspace{14mu} {gv}_{x,y,1}}\end{matrix}{is}\mspace{14mu} {saturating}} \right.$

The black level is noisy and the iterative image acquisition processobtains an image that is “less noisy” (i.e. an image with a higherconfidence level). The black value is a default value that is notrecalculated during image acquisition. The black value is a function ofexposure time.

SNR=0 when a pixel is saturating for a given exposure time (hence noimprovement in SNR) and light source intensity. Only values from thenon-saturated pixels are updated.

N=1: The initial exposure time is the best known default exposure time(a priori), or an arbitrary value

$\left( {{e.g.\text{:}}\mspace{14mu} \frac{{{Max}\mspace{14mu} {exposure}\mspace{14mu} {time}} + {{Min}\mspace{14mu} {Exposure}\mspace{14mu} {time}}}{2}} \right).$

This is determined from calibration for the particular plate and mediafor the new plate under analysis.

Grey value, reference exposure time and signal to noise ratio areupdated after each new image acquisition (i.e. N=2, 3, 4 . . . N)according to the following embodiment. Grey value gv_(x,y,N) for imageposition (x,y) corresponds to the Nth image capture of the plate usingexposure time E_(N) and respective Signal to Noise Ratio (SNR_(x,y,N)).In this embodiment:

-   -   black_(x,y,E) _(N) is the black reference value point in (x,y)        corresponding to exposure time E_(N);    -   E′_(x,y,N) is the updated reference time point in (x,y) after N        acquisitions;    -   gv′_(x,y,N,E) _(N) is the updated grey value in (x,y) after N        acquisitions at E′_(x,y,N) equivalent exposure time; and    -   SNR′_(x,y,N) is the updated SNR in x,y after N acquisitions.

$E_{x,y,N}^{\prime} = \left\{ {{\begin{matrix}{{MIN}\left( {E_{x,y,{N - 1},}^{\prime}E_{N}} \right)} & {{if}\mspace{14mu} {gv}_{x,y,{N - 1},E_{x,y,{N - 1}}}^{\prime}\mspace{14mu} {or}\mspace{14mu} {gv}_{x,y,N}\mspace{14mu} {are}\mspace{14mu} {saturating}} \\{{MAX}\left( {E_{x,y,{N - 1},}^{\prime}E_{N}} \right)} & {else}\end{matrix}{gv}_{x,y,N,E_{x,y,N}^{\prime}}^{\prime}} = {{E_{x,y,N^{X}}^{\prime}\frac{{\frac{{gv}_{x,y,{N - 1},E_{x,y,{N - 1}}^{\prime}}^{\prime}}{E_{x,y,{N - 1}}^{\prime}} \times {SNR}_{x,y,{N - 1}}^{\prime 2}} + {\frac{{gv}_{x,y,N} - {black}_{x,y,E_{N}}}{E_{N}} \times {SNR}_{x,y,N}^{2}}}{{SNR}_{x,y,{N - 1}}^{\prime 2} + {SNR}_{x,y,N}^{2}}\mspace{20mu} {SNR}_{x,y,N}^{\prime}} = \sqrt{{SNR}_{x,y,{N - 1}}^{\prime} + {SNR}_{x,y,N}^{2}}}} \right.$

Therefore, the updated SNR for a pixel in the Nth image acquisition isthe square root of the squared updated signal to noise ratio of theprior image acquisition and the squared signal to noise ratio of thecurrent image acquisition. Each acquisition provides an updated value(e.g. E′_(x,y,N)) for each pixel. That updated value is then used tocalculate the updated value for the next image acquisition. SNR=0 for apixel when a pixel is saturating for a given exposure time and lightsource intensity. Only the non-saturated pixels are updated. The N^(th)exposure time corresponds to a supervised optimization the goal of whichis to maximize the SNR for the objects of interest. The object ofinterest can be the entire plate, the colonies, a portion of the plate,or the whole image.

After updating the image data with a new acquisition, the acquisitionsystem is able to propose the best next acquisition time that wouldmaximize SNR according to environmental constraints (minimum requiredSNR, saturation constraints, maximum allowed acquisition time, region ofinterest). In embodiments where image acquisition is supervised: x,y ∈object implies that in supervised mode, the object pixels only areconsidered for the evaluations. In those embodiments where imageacquisition is not supervised, the default object is the entire image.

With reference to FIG. 7, from the acquired image analysis, the exposuretime for the next image (N+1) in the image acquisition series at a giventime interval is determined using either the automatic mode orsupervised mode described above. Referring to FIG. 7, for the automatedprocess, each pixel is weighted equally (i.e. assigned a value of 1).For the supervised approach, pixels associated with objects (e.g.cultures) are weighted differently. The supervised process requiresadditional imaging steps. If a significant fraction (e.g. greater than 1in 100,000) of pixels are saturating and their weights are not 0, then anew exposure time is proposed that is shorter (e.g. ⅕th) than theprevious minimum exposure time used to capture the image. Thisadjustment improves the probability of getting non-saturated informationfor the saturating pixels. In alternative embodiments a new exposuretime is calculated. If there is no significant pixel saturation, then,for each pixel, from the exposure and intensity map, the maximumexposure time that will not result in pixel saturation is determined.From this an exposure time for the image is determined, and an intensityimage is simulated. From this, the corresponding weighted SNR map isdetermined.

Referring to FIG. 8, the specimen image is used to update the imagedata, pixel by pixel, in the image map. That specimen data is then fedto the image analyzer and image analysis is performed informed bypredetermined constraints on the SNR for each pixel, other saturationconstraints, object constraints, etc. and time or traffic constraints(i.e. the duration of the capture and analysis).

In one embodiment specifically, the acquired image is analyzed pixel bypixel for saturated pixels. If E_(N) results in pixel saturation thatexceeds predetermined limits, a lower value for E_(N+1) is selected. Forexample, if the minimal exposure time was not acquired yet and the % ofsaturated pixels (gv′_(x,y,N,E′) _(x,y,N) =gv_(sat)) exceeds thepredetermined limit (e.g. > 1/10⁵) a new exposure time is proposed at apredetermined increment (e.g. a fifth of the minimal exposure timepreviously used). The lower limit (i.e. the minimum acceptable exposuretime) is also predetermined. These constraints on exposure time permitfaster convergence towards non-saturating image acquisition conditions.

A new image is acquired at the new exposure time. For the new image,secondary checked constraints are the minimum desired SNR per pixel(this is the lower SNR threshold) and overall acquisition time (orN_(max)) allowed for this image. If the overall acquisition time forthis scene has reached the time limit or if every updated SNR for eachpixel is such that SNR′_(x,y,N)≥MinSNR, then the image data isconsidered acceptable and the acquisition of the scene ends for the timeinterval (e.g. t₀). When image acquisition commences at time t_(x) (e.g.time t₁) the best exposure time E_(Nfinal)) leading to sub-saturationconditions from the previous acquisition (e.g. at time t₀) exposure isused as the initial value for E. The process for image acquisition att_(x) is otherwise identical to the process at time t₀.

If the saturation constraint is lifted (no significant saturation) thenext optimal exposure time is determined and investigated. First, theexposure time boundary limits are computed over the region of interest.These exposure time boundaries are: i) the exposure time to saturate thebrightest pixels; and ii) the exposure time to saturate the darkestpixels.

The exposure time for saturating the brightest non-saturated pixels,E_(min) is determined from the grey value gv_(max) that corresponds tothe absolute maximum intensity and E′_(gv) _(max) (its related exposuretime) from the following:

${gv}_{{ma}\; x} = {{gv}_{x,y,N,E_{x,y,N}^{\prime}}^{\prime}\mspace{14mu} {with}\mspace{14mu} \left\{ {\begin{matrix}{\frac{{gv}_{x,y,N,E_{x,y,N}^{\prime}}^{\prime}}{E_{x,y,N}^{\prime}}\mspace{14mu} {is}\mspace{14mu} {Maximum}} \\{{gv}_{x,y,N,E_{x,y,N}^{\prime}}^{\prime} \neq {gv}_{sat}}\end{matrix},{E_{{gv}_{{ma}\; x}}^{\prime} = {{{gv}_{{ma}\; x}\mspace{14mu} {related}\mspace{14mu} E_{x,y,N}^{\prime}E_{m\; i\; n}} = {E_{{gv}_{m\; {ax}}}^{\prime} \times \frac{{gv}_{sat}}{\max \left( {{gv}_{{ma}\; x},1} \right)}}}}} \right.}$

The exposure time for saturating the darkest pixels, E_(max) isdetermined from the grey value gv_(min) that corresponds to the absoluteminimum intensity and E′_(gv) _(min) is its related exposure time:

${gv}_{m\; i\; n} = {{gv}_{x,y,N,E_{x,y,N}^{\prime}}^{\prime}\mspace{14mu} {with}\mspace{14mu} \left\{ {\begin{matrix}{\frac{{gv}_{x,y,N,E_{x,y,N}^{\prime}}^{\prime}}{E_{x,y,N}^{\prime}}\mspace{14mu} {is}\mspace{14mu} {Minimum}} \\{{gv}_{x,y,N,E_{x,y,N}^{\prime}}^{\prime} \neq {gv}_{sat}}\end{matrix},{E_{{gv}_{m\; i\; n}}^{\prime} = {{{gv}_{m\; i\; n}\mspace{14mu} {related}\mspace{14mu} E_{x,y,N}^{\prime}E_{{ma}\; x}} = {E_{{gv}_{m\; i\; n}}^{\prime} \times \frac{{gv}_{sat}}{\max \left( {{gv}_{m\; i\; n},1} \right)}}}}} \right.}$

The next optimal exposure time is chosen among all candidate exposuretimes within E_(max) and E_(min) by simulation. Specifically, anexposure time is determined by simulation that will maximize the updatedmean SNR (for all pixels below the minimum signal to noise ratiothreshold), after adding the simulated image at tested exposure timeE_(test,N+1). The simulated image at E_(test,N+1) is generated asfollows (for each and every pixel).

Grey value gv′_(x,y,N,E′) _(x,y,N) is pixel data corresponding to thecurrent updated image data. If a new time point E_(test,N+1) isselected, the expected grey value is:

${gv}_{x,y,E_{{test},{N + 1}}} = {\min \left( {{{{gv}_{x,y,N,E_{x,y,N}^{\prime}}^{\prime} \times \frac{E_{{test},{N + 1}}}{E_{x,y,N}^{\prime}\;}} + {black}_{x,y,E_{{test},{N + 1}}}},{gv}_{sat}} \right)}$

After updating this value with a value for the pixel from the simulatedimage at time point E_(test,N+1) image, the SNR for this (x,y) pixelwill be:

SNR′_(x,y,N+1)=√{square root over (SNR′_(x,y,N) ²+SNR_(x,y,N+1) ²)}

The next best exposure time E_(best,N+1) is then determined by:

E _(best,N+1) =E _(test,N+1)∈[E _(min) ,E _(max)];

with E_(x,y ∈object) ^(E) ^(test,N+1) SNR′_(x,y,N+1) being maximum.If image acquisition and analysis is supervised x,y ∈ object the SNR isintegrated for the objects of interest only. In automatic mode theobject is the whole image.

FIG. 9 describes the final steps for image acquisition. Those steps areconventional image processing techniques well known to one skilled inthe art and not described in detail herein.

FIG. 10 illustrates the method by which system integrity is determinedduring image acquisition. Note that, once system integrity is checked,specimens are loaded into the system and the data from the specimens iscaptured. The data capture is informed by the calibration information asdiscussed above. The captured data is provided to both the systemintegrity check and a system events analyzer.

Once the image has been obtained as described above it is compared withan image of the plate that has been incubated for a different amount oftime. For example, an image of a plate is obtained as described hereinafter the plate has been incubated for four hours (T₁=4). After four ormore hours, another image of the plate is obtained as described above(T_(x)=8 hrs). The high SNR image obtained at T_(x+1) can then becompared with the high SNR image at T_(x). Changes in the two images areevaluated to ascertain evidence of microbial growth. Decisions onfurther processing (e.g. plate is positive, plate is negative, platerequires further incubation) are based on this comparison.

Although the invention herein has been described with reference toparticular embodiments, it is to be understood that these embodimentsare merely illustrative of the principles and applications of thepresent invention. It is therefore to be understood that numerousmodifications may be made to the illustrative embodiments and that otherarrangements may be devised without departing from the spirit and scopeof the present invention as defined by the appended claims.

1-14. (canceled)
 15. A system for imaging biological samples disposed ona plated culture media, the system comprising: a system calibrationmodule that provides default values for capturing an image of abiological sample disposed on culture media disposed in a plate; animage acquisition module comprising a camera wherein the imageacquisition module is adapted to acquire image data for a series ofimages at a given time interval, in communication with the systemcalibration module, the image acquisition module configured to: i)acquire data for a first image using default values for photon flux andexposure time from the system calibration module and creating a pixel bypixel map of the image data, each pixel associated with a signal tonoise ratio, a photon flux and exposure time, and an intensity; ii)update the image acquisition by reviewing the image data to identifysaturated pixels and selecting one of a new photon flux value, a newexposure time value or both based on whether a ratio of saturated tonon-saturated pixels is greater than or less than a predeterminedsaturation threshold and, based on that determination; iii) use the newvalue for the photon flux, the exposure time or both to acquire data fora new image and iv) update the pixel by pixel map of the image data witha new value for signal to noise ratio, the new photon flux value, thenew exposure time value or both and pixel intensity; wherein the systemcalibration module comprises a library of plate types and media typesand default values for photon flux and exposure time correlated to atleast one plate type and at least one media type in the library; whereinthe image acquisition module is configured to acquire data for a newimage if a signal to noise ratio of unsaturated pixels is less than apredetermined SNR threshold or a number of saturated pixels exceeds thepredetermined saturation threshold; wherein the image acquisition modulefinalizes image acquisition when either the signal to noise ratio ofunsaturated pixels meets or exceeds the predetermined SNR threshold, apredetermined allocated time for image acquisition has elapsed or apredetermined maximum number of images has been acquired; and an imagepresentation module that transforms the image data from the imageacquisition module into an image for viewing or analysis.
 16. The systemof claim 15 wherein the photon flux is a set value and the systemcontrols a camera sensor integration by controlling exposure time. 17.The system of claim 15 wherein signal to noise ratio is determined forat least a portion of the image of the biological sample disposed on theplated culture media.
 18. The system of claim 15 wherein the imageacquisition module acquires image data from the camera for at least oneor more channels or one or more spectral bands.
 19. The system of claim15 wherein the image acquisition module assigns a grey value for eachpixel for each image acquisition, and the grey value for each pixel isupdated after each image acquisition.
 20. The system of claim 19 whereinthe updated grey value is a previous grey value minus a predeterminedreference value, wherein the predetermined reference value is apredetermined value based on the plate, the plated media and theexposure time.
 21. The system of claim 16 wherein the new value for thephoton flux is obtained by using either a new exposure time or a newlight intensity value or both.
 22. The system of claim 15 wherein theimage acquisition module is configured to operate in at least one of anautomatic mode where all pixels are treated equally or a supervisedmode, where the pixels in the image provided for analysis are thosehaving been identified as associated with one or more objects ofinterest.
 23. The system of claim 15 wherein the photon flux is a setvalue and the system controls a camera sensor integration by controllingexposure time.
 24. A method for imaging biological samples disposed inculture media, the method comprising: determining default values forobtaining an image of a biological sample disposed on culture mediasupported in a plate wherein the default values are a function of theculture media and the plate supporting the culture media; acquiringimage data corresponding to a series of images at a first time over afirst time interval, the data of a first image in the series of imagesbeing acquired using predetermined default values for photon flux andexposure time; creating a pixel by pixel map of the acquired data;associating data for each pixel with a signal to noise ratio, a valuefor photon flux, exposure time, and an intensity; updating at least onevalue for the image photon flux and exposure time by: i) reviewing theacquired data for saturated pixels and the signal to noise ratio for thepixels and selecting a new value for at least one of photon flux andexposure time based on whether a ratio of saturated to non-saturatedpixels is greater than or less than a predetermined threshold andwhether a signal to noise ratio of unsaturated pixels meets or exceeds apredetermined SNR threshold, and based on that determination, using thenew value for photon flux, exposure time, or both to acquire a new imageand updating the pixel by pixel map of the image with the new values forsignal to noise ratio, photon flux and exposure time, and pixelintensity; ii) acquiring data for the new image using the at least onenew value for photon flux and exposure time; iii) optionally repeatingsteps i) and ii); finalizing image data acquisition for a time intervalwhen either the image data is at or above the predetermined SNRthreshold, a predetermined maximum allotted time for image acquisitionhas elapsed or a predetermined maximum number of images has beenacquired; and repeating the steps of acquiring, creating, associating,updating and finalizing for a second time over a second time interval;transforming the image data acquired at first and second time intervalsinto first and second images the first image acquired at the first timeand the second image acquired at a second time; comparing pixels in thefirst image and the second image; identifying pixels in the second imagethat changed from the first image to the second image; and determiningif the plate is positive, negative or requires further incubation basedon the comparison.
 25. The method of claim 24 wherein the value forphoton flux is constant and the exposure time value updated.
 26. Themethod of claim 24 further comprising determining the pixels for whichan image will be created, wherein the pixels are associated with anobject of interest.
 27. The method of claim 26 wherein the defaultvalues comprise a black level for the pixels associated with an objectof interest at a default exposure time.
 28. The method of claim 24wherein the pixel by pixel map is a grey value, the signal to noiseratio and the exposure time for each pixel.